• Journal of the Korea Society for Simulation
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• v.22 no.4
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• pp.67-82
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• 2013

This paper presents multi-fidelity modeling and simulation (M&S) methodology to enhance simulation performance of engineering-level defense models. In this approach, a set of models with varying degrees of fidelity is exercised to reduce computational expense maintaining a similar level of system effectiveness. For multi-fidelity M&S principles, this paper defines model fidelity from two perspectives (i.e., model behavior and execution), and suggests the Fidelity Change Point (FCP) to specify the fidelity conversion. With these concepts, this paper centers on three ideas: 1) two models' structure which are the Behavioral-Fidelity Interchangeable Model (B-FIM) and the Executional-Fidelity Interchangeable Model (E-FIM), 2) modeling formalism, and 3) a simulation algorithm to support them. From an abstract case study regarding a target tracking scenario with the utilization of the proposed method, we can gain interesting experimental results regarding the enhancement of simulation performance. Finally, we expect that this work will serve various M&S-based analysis areas for enhancing simulation performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.341-363
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• 2016

Haze or fog is a common natural phenomenon. In foggy weather, the captured pictures are difficult to be applied to computer vision system, such as road traffic detection, target tracking, etc. Therefore, the image dehazing technique has become a hotspot in the field of image processing. This paper presents an overview of the existing achievements on the image dehazing technique. The intent of this paper is not to review all the relevant works that have appeared in the literature, but rather to focus on two main works, that is, image dehazing scheme based on atmospheric veil and image dehazing scheme based on dark channel prior. After the overview and a comparative study, we propose an improved image dehazing method, which is based on two image dehazing schemes mentioned above. Our image dehazing method can obtain the fog-free images by proposing a more desirable atmospheric veil and estimating atmospheric light more accurately. In addition, we adjust the transmission of the sky regions and conduct tone mapping for the obtained images. Compared with other state of the art algorithms, experiment results show that images recovered by our algorithm are clearer and more natural, especially at distant scene and places where scene depth jumps abruptly.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.3
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• pp.372-375
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• 2014

A monopulse based on maximum likelihood(ML) in jamming scenario can suppress jamming signal using an inverse matrix of a covariance matrix. In order to achieve adequate suppression of jamming signal, the sufficient number of snapshots is required. However, this is not possible in high PRF scenario, which hinders a real-time tracking. Moreover, even with the large number of snapshots, the estimation accuracy of the target direction is decreased in low JNR(Jammer to Noise Ratio) due to insufficient jammer suppression. In this paper, we propose a monopulse algorithm that doesn't degrade performance significantly with a small number of snapshots and in low JNR. We show its derivation that exploits noise-jammer subspace of a covariance matrix, along with its performance through simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.378-385
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• 2000

In this paper, we consider to apply of 2-DOF (Degree of Freedom) PID controller at D.C servo motor system. Many control system use I-PD , PID control system. but the position control system have difficulty in controling variable load and changing parameter. We propose neural network 2-DOF PID control system having feature for removal disturbrances and tracking function in the target value point. The back propagation algorithm of neural network used for tuning the 2-DOF parameter(${\alpha}$,${\beta}$,${\gamma}$,η). We investigate the 2-DOF PID control system in the position control system and verify the effectiveness of proposal method through the result of computer simulation.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.5
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• pp.14-21
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• 2011

In diesel engines, accurate EGR control is important due to its effect on nitrogen oxide and particulate matter emissions. Conventional EGR control system comprises a PI feedback controller for tracking target air mass flow and a feedforward controller for fast response. Physically, the EGR flow is affected by EGR valve lift and thermodynamic properties of the EGR path, such as pressures and temperatures. However, the conventional feedforward control output is indirectly derived from engine operating conditions, such as engine rotational speed and fuel injection quantity. Accordingly, the conventional feedforward control action counteracts the feedback controller in certain operating conditions. In order to improve this disadvantage, in this study, we proposed feedforward EGR control algorithm based on a physical model of the EGR system. The proposed EGR control strategy was validated with a 3.0 liter common rail direct injection diesel engine equipped with a DC motor type EGR valve.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.6
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• pp.812-819
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• 2012

Interference and jamming are becoming increasing concern to a radar system nowdays. AESA(Active Electronically Steered Array) antennas and adaptive beamforming(ABF), in which antenna beam patterns can be modified to reject the interference, offer a potential solution to overcome the problems encountered. In this paper, we've developed a planar active phased array radar system, in which ABF, target detection and tracking algorithm operate in real-time. For the high output power and the low noise figure of the antenna, we've designed the S-band TRMs based on GaN HEMT. For real-time processing, we've used wavelenth division multiplexing technique on fiber optic communication which enables rapid data communication between the antenna and the signal processor. Also, we've implemented the HW and SW architecture of Real-time Signal Processor(RSP) for adaptive beamforming that uses SMI(Sample Matrix Inversion) technique based on MVDR(Minimum Variance Distortionless Response). The performance of this radar system has been verified by near-field and far-field tests.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1706-1727
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• 2023

Under Water Sensor Networks (UWSN) has gained attraction among various communities for its potential applications like acoustic monitoring, 3D mapping, tsunami detection, oil spill monitoring, and target tracking. Unlike terrestrial sensor networks, it performs an acoustic mode of communication to carry out collaborative tasks. Typically, surface sink nodes are deployed for aggregating acoustic phenomena collected from the underwater sensors through the multi-hop path. In this context, UWSN is constrained by factors such as lower bandwidth, high propagation delay, and limited battery power. Also, the vulnerabilities to compromise the aquatic environment are in growing numbers. The paper proposes an Energy-Efficient standalone Intrusion Detection System (EEIDS) to entail the acoustic environment against malicious attacks and improve the network lifetime. In EEIDS, attributes such as node ID, residual energy, and depth value are verified for forwarding the data packets in a secured path and stabilizing the nodes' energy levels. Initially, for each node, three agents are modeled to perform the assigned responsibilities. For instance, ID agent verifies the node's authentication of the node, EN agent checks for the residual energy of the node, and D agent substantiates the depth value of each node. Next, the classification of normal and malevolent nodes is performed by determining the score for each node. Furthermore, the proposed system utilizes the sheep-flock heredity algorithm to validate the input attributes using the optimized probability values stored in the training dataset. This assists in finding out the best-fit motes in the UWSN. Significantly, the proposed system detects and isolates the malicious nodes with tampered credentials and nodes with lower residual energy in minimal time. The parameters such as the time taken for malicious node detection, network lifetime, energy consumption, and delivery ratio are investigated using simulation tools. Comparison results show that the proposed EEIDS outperforms the existing acoustic security systems.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.101-107
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• 2016

Due to the recent advancement in digital imaging technology, development of intervention equipment has become generalize. Video arbitration procedure is a process to insert a tiny catheter and a guide wire in the body, so in order to enhance the effectiveness and safety of this treatment, the high-quality of x-ray of image should be used. However, the increasing of radiation has become the problem. Therefore, the studies to improve the performance of x-ray detectors are being actively processed. Moreover, this intervention is based on the reference of the angiographic imaging and 3D medical image processing. In this paper, we propose a guidance system to support this intervention. Through this intervention, it can solve the problem of the existing 2D medical images based vessel that has a formation of cerebrovascular disease, and guide the real-time tracking and optimal route to the target lesion by intervention catheter and guide wire tool. As a result, the system was completely composed for medical image acquisition unit and image processing unit as well as a display device. The experimental environment, guide services which are provided by the proposed system Brain Phantom (complete intracranial model with aneurysms, ref H+N-S-A-010) was taken with x-ray and testing. To generate a reference image based on the Laplacian algorithm for the image processing which derived from the cerebral blood vessel model was applied to DICOM by Volume ray casting technique. $A^*$ algorithm was used to provide the catheter with a guide wire tracking path. Finally, the result does show the location of the catheter and guide wire providing in the proposed system especially, it is expected to provide a useful guide for future intervention service.

• Progress in Medical Physics
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• v.23 no.2
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• pp.91-98
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• 2012

Verification of internal organ motion during treatment and its feedback is essential to accurate dose delivery to the moving target. We developed an offline based internal organ motion verification system (IMVS) using cine EPID images and evaluated its accuracy and availability through phantom study. For verification of organ motion using live cine EPID images, a pattern matching algorithm using an internal surrogate, which is very distinguishable and represents organ motion in the treatment field, like diaphragm, was employed in the self-developed analysis software. For the system performance test, we developed a linear motion phantom, which consists of a human body shaped phantom with a fake tumor in the lung, linear motion cart, and control software. The phantom was operated with a motion of 2 cm at 4 sec per cycle and cine EPID images were obtained at a rate of 3.3 and 6.6 frames per sec (2 MU/frame) with $1,024{\times}768$ pixel counts in a linear accelerator (10 MVX). Organ motion of the target was tracked using self-developed analysis software. Results were compared with planned data of the motion phantom and data from the video image based tracking system (RPM, Varian, USA) using an external surrogate in order to evaluate its accuracy. For quantitative analysis, we analyzed correlation between two data sets in terms of average cycle (peak to peak), amplitude, and pattern (RMS, root mean square) of motion. Averages for the cycle of motion from IMVS and RPM system were $3.98{\pm}0.11$ (IMVS 3.3 fps), $4.005{\pm}0.001$ (IMVS 6.6 fps), and $3.95{\pm}0.02$ (RPM), respectively, and showed good agreement on real value (4 sec/cycle). Average of the amplitude of motion tracked by our system showed $1.85{\pm}0.02$ cm (3.3 fps) and $1.94{\pm}0.02$ cm (6.6 fps) as showed a slightly different value, 0.15 (7.5% error) and 0.06 (3% error) cm, respectively, compared with the actual value (2 cm), due to time resolution for image acquisition. In analysis of pattern of motion, the value of the RMS from the cine EPID image in 3.3 fps (0.1044) grew slightly compared with data from 6.6 fps (0.0480). The organ motion verification system using sequential cine EPID images with an internal surrogate showed good representation of its motion within 3% error in a preliminary phantom study. The system can be implemented for clinical purposes, which include organ motion verification during treatment, compared with 4D treatment planning data, and its feedback for accurate dose delivery to the moving target.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1099-1110
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• 2023

The recognition system of autonomous driving and robot navigation performs vision work such as object recognition, tracking, and lane detection after multi-sensor fusion to improve performance. Currently, research on a deep learning model based on the fusion of a camera and a lidar sensor is being actively conducted. However, deep learning models are vulnerable to adversarial attacks through modulation of input data. Attacks on the existing multi-sensor-based autonomous driving recognition system are focused on inducing obstacle detection by lowering the confidence score of the object recognition model.However, there is a limitation that an attack is possible only in the target model. In the case of attacks on the sensor fusion stage, errors in vision work after fusion can be cascaded, and this risk needs to be considered. In addition, an attack on LIDAR's point cloud data, which is difficult to judge visually, makes it difficult to determine whether it is an attack. In this study, image scaling-based camera-lidar We propose an attack method that reduces the accuracy of LCCNet, a fusion model (camera-LiDAR calibration model). The proposed method is to perform a scaling attack on the point of the input lidar. As a result of conducting an attack performance experiment by size with a scaling algorithm, an average of more than 77% of fusion errors were caused.